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This study presents a novel zero-shot user-defined keyword spotting modelthat utilizes the audio-phoneme relationship of the keyword to improveperformance. Unlike the previous approach that estimates at utterance level, weuse both utterance and phoneme level information. Our proposed method comprisesa two-stream speech encoder architecture, self-attention-based patternextractor, and phoneme-level detection loss for high performance in variouspronunciation environments. Based on experimental results, our proposed modeloutperforms the baseline model and achieves competitive performance comparedwith full-shot keyword spotting models. Our proposed model significantlyimproves the EER and AUC across all datasets, including familiar words, propernouns, and indistinguishable pronunciations, with an average relativeimprovement of 67% and 80%, respectively. The implementation code of ourproposed model is available at https://github.com/ncsoft/PhonMatchNet.

PhonMatchNet: Phoneme-Guided Zero-Shot Keyword Spotting for User-Defined Keywords